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April 21, 2026, 9:20 p.m.
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| Boruah A. Embedded Artificial Intelligence. Real-Life Apps and Case Studies 2025.pdf | 9.7 MB |
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SOURCE: Boruah A. Embedded Artificial Intelligence. Real-Life Apps and Case Studies 2025
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MEDIAINFO
Textbook in PDF format This book explores the role of embedded AI in revolutionizing industries such as healthcare, transportation, manufacturing, and retail. It begins by introducing the fundamentals of AI and embedded systems and specific challenges and opportunities. A key focus of this book is developing efficient and effective algorithms and models for embedded AI systems, as embedded systems have limited processing power, memory, and storage. It discusses a variety of techniques for optimizing algorithms and models for embedded systems, including hardware acceleration, model compression, and quantization. Embedded systems, which are distinguished by their small footprint, restricted resources, and ability to operate in real time, are progressively evolving into centres of intelligence by incorporating Machine Learning algorithms. This introductory chapter delves into the convergence of machine learning and embedded systems, elucidating the complexities associated with the implementation of sophisticated algorithms on devices that have limited resources. Commencing with an examination of the Machine Learning algorithms that are appropriate for embedded systems, we assess their capability to accommodate the distinctive limitations of these platforms. Recognizing the significance of implementing Machine Learning on devices with limited resources, we explore the ramifications across multiple sectors, such as automotive, consumer electronics, IoT, and healthcare. Our objective is to outline the limitations, intriguing benefits, and optimal strategies to enhance embedded intelligence. Optimizing Machine Learning models for deployment on embedded platforms requires striking a balance between model complexity and performance. Additionally, real-time processing requirements and stringent latency constraints further complicate the integration of Machine Learning algorithms into embedded systems. These systems rely on low-power microcontrollers or microprocessors optimized for specific tasks, alongside integrated sensors and actuators. These architectures prioritize compactness, efficiency, and real-time responsiveness, facilitating seamless integration into various embedded applications such as IoT devices, automotive systems, and industrial control systems. Key features: Explores security experiments in emerging post?CMOS technologies using AI, including side channel attack?resistant embedded systems. Discusses different hardware and software platforms available for developing embedded AI applications, as well as the various techniques used to design and implement these systems. Considers ethical and societal implications of embedded AI vis?a?vis the need for responsible development and deployment of embedded AI systems. Focuses on application?based research and case studies to develop embedded AI systems for real?life applications. Examines high?end parallel systems to run complex AI algorithms and comprehensive functionality while maintaining portability and power efficiency. This reference book is for students, researchers, and professionals interested in embedded AI and relevant branches of Computer Science, electrical engineering, or Artificial Intelligence
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